{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recurrent Neural Networks Models\n",
    "In this notebook, we show an example of how RNNs can be used with darts.\n",
    "If you are new to darts, we recommend you first follow the `darts-intro.ipynb` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shutil\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import RNNModel, ExponentialSmoothing, BlockRNNModel\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf\n",
    "from darts.datasets import AirPassengersDataset, SunspotsDataset\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrent Models\n",
    "\n",
    "Darts includes two recurrent forecasting model classes: `RNNModel` and `BlockRNNModel`. \n",
    "\n",
    "`RNNModel` is fully recurrent in the sense that, at prediction time, an output is computed using these inputs:\n",
    "- the previous target value, which will be set to the last known target value for the first prediction,\n",
    "  and for all other predictions it will be set to the previous prediction\n",
    "- the previous hidden state\n",
    "- the current covariates (if the model was trained with covariates)\n",
    "\n",
    "A prediction with forecasting horizon `n` thus is created in `n` iterations of `RNNModel` predictions and requires `n` future covariates to be known. This model is suited for forecasting problems where the target series is highly dependent on covariates that are known in advance.\n",
    "\n",
    "`BlockRNNModel` has a recurrent encoder stage, which encodes its input, and a fully-connected neural network decoder stage, which produces a prediction of length `output_chunk_length` based on the last hidden state of the encoder stage. Consequently, this model produces 'blocks' of forecasts and is restricted to looking at covariates with the same time index as the input target series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Air Passenger Example\n",
    "This is a data set that is highly dependent on covariates. Knowing the month tells us a lot about the seasonal component, whereas the year determines the effect of the trend component. Both of these covariates are known in the future, and thus the `RNNModel` class is the preferred choice for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data:\n",
    "series = AirPassengersDataset().load()\n",
    "\n",
    "# Create training and validation sets:\n",
    "train, val = series.split_after(pd.Timestamp('19590101'))\n",
    "\n",
    "# Normalize the time series (note: we avoid fitting the transformer on the validation set)\n",
    "transformer = Scaler()\n",
    "train_transformed = transformer.fit_transform(train)\n",
    "val_transformed = transformer.transform(val)\n",
    "series_transformed = transformer.transform(series)\n",
    "\n",
    "# create month and year covariate series\n",
    "year_series = datetime_attribute_timeseries(pd.date_range(start=series.start_time(), freq=series.freq_str, periods=1000),\n",
    "                                             attribute='year', one_hot=False)\n",
    "year_series = Scaler().fit_transform(year_series)\n",
    "month_series = datetime_attribute_timeseries(year_series, attribute='month', one_hot=True)\n",
    "covariates = year_series.stack(month_series)\n",
    "cov_train, cov_val = covariates.split_after(pd.Timestamp('19590101'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train an LSTM neural net. For using vanilla RNN or GRU instead, replace `'LSTM'` by `'RNN'` or `'GRU'`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "my_model = RNNModel(\n",
    "    model='LSTM',\n",
    "    hidden_dim=20,\n",
    "    dropout=0,\n",
    "    batch_size=16,\n",
    "    n_epochs=300,\n",
    "    optimizer_kwargs={'lr': 1e-3}, \n",
    "    model_name='Air_RNN',\n",
    "    log_tensorboard=True,\n",
    "    random_state=42,\n",
    "    training_length=20,\n",
    "    input_chunk_length=14,\n",
    "    force_reset=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what follows, we can just provide the whole `covariates` series as `future_covariates` argument to the model; the model will slice these covariates and use only what it needs in order to train on forecasting the target `train_transformed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "372c4710d2b243b19fe2b5897790700e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0003, validation loss: 0.0021, best val loss: 0.0021\r"
     ]
    }
   ],
   "source": [
    "my_model.fit(train_transformed, \n",
    "             future_covariates=covariates, \n",
    "             val_series=val_transformed, \n",
    "             val_future_covariates=covariates, \n",
    "             verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at predictions on the validation set\n",
    "Use the \"current\" model - i.e., the model at the end of the training procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def eval_model(model):\n",
    "    pred_series = model.predict(n=26, future_covariates=covariates)\n",
    "    plt.figure(figsize=(8,5))\n",
    "    series_transformed.plot(label='actual')\n",
    "    pred_series.plot(label='forecast')\n",
    "    plt.title('MAPE: {:.2f}%'.format(mape(pred_series, val_transformed)))\n",
    "    plt.legend();\n",
    "    \n",
    "eval_model(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the best model obtained over training, according to validation loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading model_best_290.pth.tar\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = RNNModel.load_from_checkpoint(model_name='Air_RNN', best=True)\n",
    "eval_model(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting\n",
    "Let's backtest our `RNN` model, to see how it performs at a forecast horizon of 6 months:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43013c6c4d3e4629b73bedd551ea8694",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backtest_series = my_model.historical_forecasts(series_transformed,\n",
    "                                                future_covariates=covariates,\n",
    "                                                start=pd.Timestamp('19590101'),\n",
    "                                                forecast_horizon=6,\n",
    "                                                retrain=False,\n",
    "                                                verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 2.43%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "series_transformed.plot(label='actual')\n",
    "backtest_series.plot(label='backtest')\n",
    "plt.legend()\n",
    "plt.title('Backtest, starting Jan 1959, 6-months horizon');\n",
    "print('MAPE: {:.2f}%'.format(mape(transformer.inverse_transform(series_transformed), \n",
    "                                  transformer.inverse_transform(backtest_series))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly sunspots\n",
    "Let's now try a more challenging time series; that of the monthly number of sunspots since 1749. First, we build the time series from the data, and check its periodicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 125)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_sunspot = SunspotsDataset().load()\n",
    "\n",
    "series_sunspot.plot()\n",
    "check_seasonality(series_sunspot, max_lag=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(series_sunspot, 125, max_lag=240) # ~11 years seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sp, val_sp = series_sunspot.split_after(pd.Timestamp('19401001'))\n",
    "\n",
    "transformer_sunspot = Scaler()\n",
    "train_sp_transformed = transformer_sunspot.fit_transform(train_sp)\n",
    "val_sp_transformed = transformer_sunspot.transform(val_sp)\n",
    "series_sp_transformed = transformer_sunspot.transform(series_sunspot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca88d50adb394671952d7814cdac4510",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0087, validation loss: 0.0208, best val loss: 0.0208\r"
     ]
    }
   ],
   "source": [
    "my_model_sun = BlockRNNModel(\n",
    "    model='GRU',\n",
    "    input_chunk_length=125,\n",
    "    output_chunk_length=36,\n",
    "    hidden_size=10,\n",
    "    n_rnn_layers=1,\n",
    "    batch_size=32,\n",
    "    n_epochs=100,\n",
    "    dropout=0.1,\n",
    "    model_name='sun_GRU',\n",
    "    nr_epochs_val_period=1,\n",
    "    optimizer_kwargs={'lr': 1e-3},\n",
    "    log_tensorboard=True,\n",
    "    random_state=42,\n",
    "    force_reset=True\n",
    ")\n",
    "\n",
    "my_model_sun.fit(train_sp_transformed, val_series=val_sp_transformed, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate our model, we will simulate historic forecasting with a forecasting horizon of 3 years across the validation set. To speed things up, we will only look at every 10th forecast. For the sake of comparison, let's also fit an exponential smoothing model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43fd8d1d751349d4849ccb1fe497a91d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d8ab12d29db4cc39601905ed6ae7a30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the backtest predictions with the two models\n",
    "pred_series = my_model_sun.historical_forecasts(series_sp_transformed,\n",
    "                                                start=pd.Timestamp('19401001'), \n",
    "                                                forecast_horizon=36,\n",
    "                                                stride=10,\n",
    "                                                retrain=False,\n",
    "                                                last_points_only=True,\n",
    "                                                verbose=True)\n",
    "\n",
    "pred_series_ets = ExponentialSmoothing(seasonal_periods=120).historical_forecasts(series_sp_transformed,\n",
    "                                                                                  start=pd.Timestamp('19401001'), \n",
    "                                                                                  forecast_horizon=36,\n",
    "                                                                                  stride=10,\n",
    "                                                                                  retrain=True,\n",
    "                                                                                  last_points_only=True,\n",
    "                                                                                  verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNN MAPE: 71.86777453068686\n",
      "ETS MAPE: 116.67300879897265\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_sp_transformed.plot(label='actual')\n",
    "pred_series.plot(label='our RNN')\n",
    "pred_series_ets.plot(label='ETS')\n",
    "plt.legend();\n",
    "print(\"RNN MAPE:\", mape(pred_series, val_sp_transformed))\n",
    "print(\"ETS MAPE:\", mape(pred_series_ets, val_sp_transformed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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